code: def separate(
      cls: Type[T],
      batch: Any,
      idx: int,
      slice_dict: Any,
      inc_dict: Any = None,
      decrement: bool = True,
  ) -> T:
      # Separates the individual element from a `batch` at index `idx`.
      # `separate` can handle both homogeneous and heterogeneous data objects by
      # individually separating all their stores.
      # In addition, `separate` can handle nested data structures such as
      # dictionaries and lists.
  
      data = cls().stores_as(batch)
  
      # Iterate over each storage object and recursively separate its attributes:
      for batch_store, data_store in zip(batch.stores, data.stores):
          key = batch_store._key
          if key is not None:  # Heterogeneous:
              attrs = slice_dict[key].keys()
          else:  # Homogeneous:
              attrs = set(batch_store.keys())
              attrs = [
                  attr for attr in slice_dict.keys() if attr in attrs
              ]
  
          for attr in attrs:
              if key is not None:
                  slices = slice_dict[key][attr]
                  incs = inc_dict[key][attr] if decrement else None
              else:
                  slices = slice_dict[attr]
                  incs = inc_dict[attr] if decrement else None
  
              data_store[attr] = _separate(
                  attr,
                  batch_store[attr],
                  idx,
                  slices,
                  incs,
                  batch,
                  batch_store,
                  decrement,
              )
  
          # The `num_nodes` attribute needs special treatment, as we cannot infer
          # the real number of nodes from the total number of nodes alone:
          if hasattr(batch_store, "_num_nodes"):
              data_store.num_nodes = batch_store._num_nodes[idx]
  
      return data
  
file_path: pytorch_geometric/torch_geometric/data/separate.py
imports: from collections.abc import Mapping, Sequence
  from typing import Any, Type, TypeVar
  from torch import Tensor
  from torch_geometric import EdgeIndex
  from torch_geometric.data.data import BaseData
  from torch_geometric.data.storage import BaseStorage
  from torch_geometric.typing import SparseTensor, TensorFrame
  from torch_geometric.utils import narrow
label_desc: Use image node_id 1 for calling a global function with example usage: separate(cls, batch, idx, slice_dict, inc_dict, decrement) and returns: data